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Data Fellowship

Multiverse Data Fellowship: programming, data modelling, analysis, insights, aligned to Level 4 Data Analyst apprenticeship.

Updated over a week ago

Data Fellowship Programme Overview

This programme is designed for aspiring and new data analysts, empowering teams with programming, data modelling, and analysis skills to effectively utilise business data. It is part of our Data Academy Programmes.

Key Skills Gained:

  • SQL

  • Data visualisation

  • Advanced BI

  • Machine learning

Business Outcomes:

  • Boost productivity: Increase access to clean, structured data that can be utilised by the business.

  • Improve business decision-making: Help teams leverage data in a visual context and communicate actionable insights.

  • Democratise data skills: Reduce reliance on central data teams by equipping business functions with data analysis skills.

  • Reduce costs: Lower the number of manual data tasks that are often at risk of human error.

Apprenticeship Qualification Achieved: Level 4 Data Analyst

Duration: 13 month delivery, plus 2 month assessment


Data Fellowship Indicative Curriculum Breakdown

Data analysis essentials

  • Month 1: Foundations of a Data Analyst

    • Learn the basics of data analysis and key statistical concepts, and gain familiarity with various data types and structures.

    • Develop an understanding and skills in exploratory data analysis to filter, sort and calculate descriptive statistics.

  • Month 2: Foundations of data management

    • Understand principles of data management and governance, developing skills in ensuring data accuracy and quality.

    • Maintain data integrity and compliance with regulations by developing and applying robust data governance frameworks.

Visualising and integrating data with a BI tool

  • Month 3: Visualising data for stakeholders

    • Design impactful data visualisations and present insights effectively. Tailor data stories to different audiences and improve the clarity of data for informed decision-making.

    • Develop communication skills and enhance collaboration with stakeholders.

  • Month 4: Integrating data for business impact

    • Understand database design principles and data modeling.

    • Learn techniques for expanding and integrating datasets, building efficient and scalable database systems with data accessibility and usability at its core.

  • Month 5: Data analysis hackathon and EPA prep session

    • Working together on a challenge that allows apprentices to use the skills learned so far.

    • Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.

Levelling up data analysis with statistics and AI

  • Month 6: Data integration and analysis techniques

    • Develop skills in SQL and data integration techniques, combining and manipulating data from various sources.

    • Enable seamless data integration across platforms and support greater granularity in strategic decision-making with comprehensive datasets.

  • Month 7: Advanced analytics and statistical methods

    • Understand and apply advanced statistical techniques, deriving deeper insights from data.

    • Uncover hidden trends and patterns for precise decision-making and evaluate data for use in predictive analytics.

  • Month 8: Statistics hackathon and EPA prep session

    • Working together on a challenge that allows apprentices to use the skills learned so far.

    • Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.

Machine learning and predictive analytics

  • Month 9: Predicting the future with time series forecasting

    • Analyse time series data and build forecasting models.

    • Evaluate and improve forecasting accuracy for strategic planning and anticipate trends and patterns for more effective forecasts and planning.

  • Month 10: Introduction to machine learning

    • Learn the basics of machine learning and model implementation and how to develop, train and optimise models.

    • Incorporate machine learning for more effective automation and drive innovation with predictive analytics.

  • Month 11: Machine learning hackathon

    • Working together on a challenge that allows apprentices to use the skills learned so far.

  • Month 12: End point assessment (EPA) preparation

    • Working session to learn more about the EPA, practice for interviews, and work on evidence.

Note: This is an example curriculum, and specific details may vary per cohort.


Data Fellowship Indicative Delivery Model

Monthly delivery model, approx. 27 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.

  • Structured Learning (~45% - 12 hours/month):

    • Asynchronous learning (6 hours): Online, self-paced content that sets the foundation of skills for the module.

    • Group learning (5 hours): Live, instructor-led, small-group interactive learning that dives deeper and reinforces the asynchronous content.

    • Coach and peer support (1 hour): Coach and peer support.

  • Working in Existing Role (~55% - 15 hours/month):

    • Work-based tasks (7 hours): Structured tasks to provide the opportunity to apply learnings in real work context.

    • Independent applied learning (8 hours): Application of learning to apprentices’ existing day to day activities.


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